摘要
本文给出了一种利用神经网络计算光流场的新算法。整个计算过程分为三个阶段:神经网络模型参数的估计,轮廓边界垂直速度分量的动态测量以及光流场的计算。通过网络能量函数和运动的约束误差函数的比较对网络参数进行估计。用一个动态算法迭代运行非线性光流场计算方法以使神经网络能量函数达到最小,同时也对垂直速度分量进行动态估计。由模拟试验结果讨论了影响神经网络收敛性能的若干因素。
A new algorithm for optical flow computation is presented using neural networks. The computation procedure consists of three stages: estimation of the parameters of the neural network model, dynamic measurement of the perpendicular velocity components of the contour and optical flow computation. The parameters are estimated by comparing the energy function of the neural network with a constrained error function of motion. The nonlinear optical flow computation method is then carried out iteratively by using a dynamic algorithm to minimize the energy function simultaneously with the dynamic measurement of the perpendicular velocity components by a dynamic procedure. Some factors affecting the convergence property of the neural network are discussed through the simulation results.
出处
《电子学报》
EI
CAS
CSCD
北大核心
1992年第2期64-70,共7页
Acta Electronica Sinica
基金
国家自然科学基金
